Evidence map›Paper›PMID 31701320›Full record

ReviewCurrent psychiatry reports2019

Artificial Intelligence for Mental Health and Mental Illnesses: an Overview.

Sarah Graham, Colin Depp, Ellen E Lee, Camille Nebeker, Xin Tu, Ho-Cheol Kim, Dilip V Jeste

Abstract readReview
PubMed Full text at PMC Publisher
In one paragraph

Review in Current psychiatry reports, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 279 papers, 17 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
279citing papers in PubMed, 17 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

279 citing papers in PubMed, 17 syntheses or guidelines pooled it.

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  2. Responsible artificial intelligence integration framework for psychiatric guidelines.The international journal of neuropsychopharmacology · 2026
    Guideline
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219 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

Sarah GrahamDepartment of Psychiatry, University of California San Diego, La Jolla, CA, USA.
Colin DeppDepartment of Psychiatry, University of California San Diego, La Jolla, CA, USA.
Ellen E LeeDepartment of Psychiatry, University of California San Diego, La Jolla, CA, USA.
Camille NebekerDepartment of Family Medicine and Public Health, University of California La Jolla, La Jolla, CA, USA.
Xin TuDepartment of Psychiatry, University of California San Diego, La Jolla, CA, USA.
Ho-Cheol KimScalable Knowledge Intelligence, IBM Research-Almaden, San Jose, CA, USA.
Dilip V JesteDepartment of Psychiatry, University of California San Diego, La Jolla, CA, USA. djeste@ucsd.edu.

Funding

UC San Diego Clinical and Translational Research InstituteUL1TR001442 · NCATS · UNIVERSITY OF CALIFORNIA, SAN DIEGO · PI FIRESTEIN, GARY S, HOGARTH, MICHAEL · 2015 to 2024
$88.3M
FELLOWSHIP IN GERIATRIC MENTAL HEALTHT32MH019934 · NIMH · UNIVERSITY OF CALIFORNIA, SAN DIEGO · PI Elizabeth W Twamley · 1994 to 2026
$9.5M
Inflammatory Mechanisms Linking Sleep Disturbances and Cognitive Deficits in Schizophrenia in Later LifeK23MH119375 · NIMH · UNIVERSITY OF CALIFORNIA, SAN DIEGO · PI LEE, ELLEN EUN-OK · 2019 to 2023
$1.0M
NCATS NIH HHS UL1 TR001442NIMH NIH HHS K23 MH119375NIMH NIH HHS T32 MH019934
6 · The paper itself

Abstract

purpose of reviewArtificial intelligence (AI) technology holds both great promise to transform mental healthcare and potential pitfalls. This article provides an overview of AI and current applications in healthcare, a review of recent original research on AI specific to mental health, and a discussion of how AI can supplement clinical practice while considering its current limitations, areas needing additional research, and ethical implications regarding AI technology. RECENT

findingsWe reviewed 28 studies of AI and mental health that used electronic health records (EHRs), mood rating scales, brain imaging data, novel monitoring systems (e.g., smartphone, video), and social media platforms to predict, classify, or subgroup mental health illnesses including depression, schizophrenia or other psychiatric illnesses, and suicide ideation and attempts. Collectively, these studies revealed high accuracies and provided excellent examples of AI's potential in mental healthcare, but most should be considered early proof-of-concept works demonstrating the potential of using machine learning (ML) algorithms to address mental health questions, and which types of algorithms yield the best performance. As AI techniques continue to be refined and improved, it will be possible to help mental health practitioners re-define mental illnesses more objectively than currently done in the DSM-5, identify these illnesses at an earlier or prodromal stage when interventions may be more effective, and personalize treatments based on an individual's unique characteristics. However, caution is necessary in order to avoid over-interpreting preliminary results, and more work is required to bridge the gap between AI in mental health research and clinical care.

Indexed as

Artificial IntelligenceMental DisordersMental HealthAlgorithmsHumansMachine LearningBioethicsDeep learningDepressionMachine learningNatural language processingResearch ethicsSchizophreniaSuicideTechnology

Identifiers

PMID31701320
PMCPMC7274446

What OpenQuestion holds

Textmetadata
LicenceTDM
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.